Initially I performed applied research in combinatorial optimization and operations research at the CODeS group, part of KU Leuven, Belgium, in collaboration with ArcelorMittal. Hereafter I worked in the domain of network science, designing experimental routing strategies for computer networks. Later my interests shifted to artificial intelligence and machine learning, which became my true passion.

More specifically I focused on combining deep neural networks with structured prediction. During my doctoral studies, I also acted as a researcher at the R&D institute imec, which allowed me to apply my research to visual perception in autonomous agricultural vehicles, in collaboration with CNH Industrial.

From 2017 to 2018, I was a research scientist at OpenAI in machine learning with a focus on deep reinforcement learning. I co-organized the Deep Reinforcement Learning Workshop at NIPS 2017/2018 and was involved in the Berkeley Deep RL Bootcamp.

Selected Projects

Meta-learning

Learning to learn in deep reinforcement learning (RL), including learning to explore without the use of additional structures. Below a video of a hopping robot learning to either hop forward or backward from scratch using Evolved Policy Gradients (EPG).

Deep Reinforcement Learning

Reinforcement learning (RL) using nonlinear function approximators with a focus on continuous control tasks such as robot locomotion. In particular, the goal is to investigate how to achieve efficient exploration in deep RL through curiosity. This research was performed in collaboration with OpenAI and the Berkeley AI Research lab.

Generative Models

InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. We derive a lower bound of the mutual information objective that can be optimized efficiently.

Structured Prediction and Deep Learning

As part of an autonomous vehicle project, the goal was to combine structured output prediction and deep learning techniques, with a particular focus on semantic image segmentation. Structural support vector machines (SSVMs) were extended to allow for highly nonlinear factors. This can enhance output coherence of deep predictive models, while still allowing for end-to-end training. Below the architecture of a deep SSVM with convolutional neural factors is pictured.

* This work is part of an applied research project in collaboration with Case New Holland (CNH) Industrial. As such several methods, models, datasets, and results could not be publicly released due to confidentiality agreements. An addendum to these papers can be found here. Initial vehicle controller patent applications have been filed.

Network Science

My research originally focused on the development of a novel routing algorithm called Forest Routing. Through geometric routing, using a set of graph embeddings in a particular mathematical space, it offers both high scalability and native load balancing behavior. A coherent write-up on the subject can be found in my thesis Adaptive Geometric Routing for the Internet Backbone. Below a demonstration of the developed model is shown.